Accreditation exercise for 16S rRNA data analysis

Denosing stats

require(kableExtra)
stats <- read.table("data/stats.tsv", header = T)
stats %>% kable(booktabs = TRUE, longtable = TRUE) %>% kable_styling(latex_options = c("hold_position", 
    "repeat_header", position = "")) %>% scroll_box(width = "100%", height = "300px")

mean(stats$input)

[1] 185871.8

mean(stats$filtered)

[1] 143041.2

mean(stats$merged)

[1] 137931.7

mean(stats$non.chimeric)

[1] 137931.7

lost_filt <- (mean(stats$input) - mean(stats$filtered))/mean(stats$input)
lost_filt

[1] 0.2304308

lost_merge <- (mean(stats$filtered) - mean(stats$merged))/mean(stats$filtered)
lost_merge

[1] 0.03572084

lost_chime <- (mean(stats$merged) - mean(stats$non.chimeric))/mean(stats$merged)
lost_chime

[1] 0

lost_overall <- (mean(stats$input) - mean(stats$non.chimeric))/mean(stats$input)
lost_overall

[1] 0.2579204

require(plotly)
df <- stats[, -c(1)]  #sample ids
p <- ggplot(stack(df), aes(x = ind, y = values)) + geom_boxplot(col = "blue", 
    outline = FALSE) + theme_bw() + ylab("Number of reads") + xlab("")
ggplotly(p)

Detected ASvs

df <- as.data.frame(sample_sums(phy))
colnames(df) <- c("Number_of_ASVs")
df %>% kable(booktabs = TRUE, longtable = TRUE) %>% kable_styling(latex_options = c("hold_position", 
    "repeat_header", position = "")) %>% scroll_box(width = "100%", height = "200px")

phy <- subset_samples(phy, !is.na(Inflammation))
phy <- subset_samples(phy, sample_sums(phy) > 5000)
phy <- subset_samples(phy, sample != "S120")
phy

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 3222 taxa and 111 samples ]
sample_data() Sample Data:       [ 111 samples by 5 sample variables ]
tax_table()   Taxonomy Table:    [ 3222 taxa by 8 taxonomic ranks ]
phy_tree()    Phylogenetic Tree: [ 3222 tips and 3218 internal nodes ]

total = median(sample_sums(phy))
standf = function(x, t = total) round(t * (x/sum(x)))
M.std = transform_sample_counts(phy, standf)

M.std = filter_taxa(M.std, function(x) sum(x > 10) > (0.01 * length(x)) | sum(x) > 
    0.001 * total, TRUE)
# M.f = filter_taxa(M.std,function(x) sum(x) > 0.001*total, TRUE)
ntaxa(M.std)

[1] 1075

require(cluster)
library(factoextra)
df1 <- data.frame(otu_table(M.std))
df_viz <- fviz_nbclust(df1, kmeans, method = "wss") + theme_minimal()
res_fanny <- fanny(t(df1), k = 3, metric = "SqEuclidean")

sample_data(M.std)$cluster <- as.character(res_fanny$clustering)

# clusters<-NULL memb_prob<-res_fanny$membership
# sample_ids<-rownames(memb_prob) for (i in 1:dim(memb_prob)[1]) {
# memb_prob_<-max(memb_prob[i,]) tmp<-data.frame(sample_ids[i], memb_prob_)
# clusters<-rbind(clusters, tmp) }

require(microbiomeSeq)
require(pheatmap)
phyTop <- taxa_level(M.std, "G_species")
TopASVs <- names(sort(taxa_sums(phyTop), TRUE))[1:50]
df1 <- as.data.frame(otu_table(t(phyTop)))
df1 <- df1[which(rownames(df1) %in% TopASVs), ]
df1 <- df1[which(rownames(df1) != " "), ]
df <- data.frame((sample_data(phyTop))[, c("Inflammation", "BV")])
p <- pheatmap(log2(df1 + 1), cluster_rows = FALSE, show_rownames = TRUE, show_colnames = F, 
    cluster_cols = TRUE, annotation_col = df, annotation_row = NA)
p

require(plotly)
p <- plot_richness(M.std, x = "cluster", color = "cluster", measures = c("Observed", 
    "Simpson"), title = paste0("Standardized to total reads"))
p <- p + geom_boxplot() + geom_point() + theme_bw() + theme(axis.text = element_text(size = 10, 
    face = "bold"), axis.title = element_text(size = 16, face = "bold")) + geom_point(size = 2)
ggplotly(p)

p <- plot_richness(M.std, x = "Inflammation", color = "Inflammation", measures = c("Observed", 
    "Simpson"), title = paste0("Standardized to total reads"))
p <- p + geom_boxplot() + geom_point() + theme_bw() + theme(axis.text = element_text(size = 10, 
    face = "bold"), axis.title = element_text(size = 16, face = "bold")) + geom_point(size = 2)
ggplotly(p)

p <- plot_richness(M.std, x = "BV", color = "BV", measures = c("Observed", "Simpson"), 
    title = paste0("Standardized to total reads"))
p <- p + geom_boxplot() + geom_point() + theme_bw() + theme(axis.text = element_text(size = 10, 
    face = "bold"), axis.title = element_text(size = 16, face = "bold")) + geom_point(size = 2)
ggplotly(p)

est <- estimate_richness(M.std, split = TRUE, measures = c("Simpson"))
BV_div <- cbind(est, sample_data(M.std)[, "BV"])
t <- wilcox.test(BV_div$Simpson ~ BV_div$BV)
t

    Wilcoxon rank sum test with continuity correction

data:  BV_div$Simpson by BV_div$BV
W = 398, p-value = 1.71e-11
alternative hypothesis: true location shift is not equal to 0

BInflammation_div <- cbind(est, sample_data(M.std)[, "Inflammation"])
t <- wilcox.test(BInflammation_div$Simpson ~ BInflammation_div$Inflammation)
t

    Wilcoxon rank sum test with continuity correction

data:  BInflammation_div$Simpson by BInflammation_div$Inflammation
W = 2204, p-value = 3.854e-05
alternative hypothesis: true location shift is not equal to 0

Cluster_div <- cbind(est, sample_data(M.std)[, "cluster"])
dunn.test::dunn.test(Cluster_div$Simpson, Cluster_div$cluster)

  Kruskal-Wallis rank sum test

data: x and group
Kruskal-Wallis chi-squared = 58.3445, df = 2, p-value = 0

                           Comparison of x by group                            
                                (No adjustment)                                
Col Mean-|
Row Mean |          1          2
---------+----------------------
       2 |  -5.906293
         |    0.0000*
         |
       3 |  -0.080297   6.501771
         |     0.4680    0.0000*

alpha = 0.05
Reject Ho if p <= alpha/2

# ordination based on NMDS and bray-curtis distance
NMDS_bray <- ordinate(M.std, "NMDS")

Square root transformation
Wisconsin double standardization
Run 0 stress 0.2055082 
Run 1 stress 0.2309574 
Run 2 stress 0.217807 
Run 3 stress 0.2221731 
Run 4 stress 0.1968815 
... New best solution
... Procrustes: rmse 0.0485541  max resid 0.2755098 
Run 5 stress 0.222773 
Run 6 stress 0.2169525 
Run 7 stress 0.2262007 
Run 8 stress 0.226073 
Run 9 stress 0.2171289 
Run 10 stress 0.2318284 
Run 11 stress 0.2278323 
Run 12 stress 0.2134497 
Run 13 stress 0.2192831 
Run 14 stress 0.2118285 
Run 15 stress 0.2277937 
Run 16 stress 0.2111281 
Run 17 stress 0.2105766 
Run 18 stress 0.4130426 
Run 19 stress 0.2136909 
Run 20 stress 0.2176685 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

title = c("NMDS of 16S microbiome, Bray-Curtis distance")

NMDS.bray.plot <- plot_ordination(M.std, NMDS_bray, color = "BV", shape = "Inflammation", 
    title = title)
NMDS.bray.plot <- NMDS.bray.plot + theme(axis.text = element_text(size = 16, 
    face = "bold"), axis.title = element_text(size = 18, face = "bold"), legend.title = element_text(size = 14)) + 
    theme_bw() + labs(color = "BV") + geom_point(size = 5)
NMDS.bray.plot

PCoA.ord.bray <- ordinate(M.std, "PCoA", "bray")
title = c("PCoA of 16S microbiome, Bray-Curtis distance")
PCoA.ord <- plot_ordination(M.std, PCoA.ord.bray, color = "cluster", shape = "BV", 
    title = title)
PCoA.ord <- PCoA.ord + theme(axis.text = element_text(size = 16, face = "bold"), 
    axis.title = element_text(size = 18, face = "bold"), legend.title = element_text(size = 14)) + 
    theme_bw() + labs(color = "cluster") + geom_point(size = 5)
PCoA.ord

# permanova
require(vegan)
phy_bray <- phyloseq::distance(M.std, method = "bray")
sampledf <- data.frame(sample_data(M.std))

adonis(phy_bray ~ BV, data = sampledf)

Call:
adonis(formula = phy_bray ~ BV, data = sampledf) 

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
BV          1     7.396  7.3956  27.198 0.19969  0.001 ***
Residuals 109    29.639  0.2719         0.80031           
Total     110    37.035                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

beta <- betadisper(phy_bray, sampledf$BV)
permutest(beta)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups      1 0.00977 0.0097732 0.5461    999  0.469
Residuals 109 1.95083 0.0178975                     

adonis(phy_bray ~ Inflammation, data = sampledf)

Call:
adonis(formula = phy_bray ~ Inflammation, data = sampledf) 

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

              Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
Inflammation   1     3.676  3.6759  12.011 0.09926  0.001 ***
Residuals    109    33.359  0.3060         0.90074           
Total        110    37.035                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

beta <- betadisper(phy_bray, sampledf$Inflammation)
permutest(beta)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups      1 0.00025 0.0002487 0.0177    999  0.904
Residuals 109 1.53154 0.0140509                     

adonis(phy_bray ~ cluster, data = sampledf)

Call:
adonis(formula = phy_bray ~ cluster, data = sampledf) 

Permutation: free
Number of permutations: 999

Terms added sequentially (first to last)

           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
cluster     2    17.856  8.9278  50.273 0.48213  0.001 ***
Residuals 108    19.179  0.1776         0.51787           
Total     110    37.035                 1.00000           
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

beta <- betadisper(phy_bray, sampledf$BV)
permutest(beta)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups      1 0.00977 0.0097732 0.5461    999  0.448
Residuals 109 1.95083 0.0178975                     

sampledf$cluster <- as.factor(sampledf$cluster)
logit_model <- glm(Inflammation ~ cluster + BMI + Age, family = binomial(link = "logit"), 
    data = sampledf)
summary(logit_model)

Call:
glm(formula = Inflammation ~ cluster + BMI + Age, family = binomial(link = "logit"), 
    data = sampledf)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8371  -0.7213  -0.6278   0.8985   1.9101  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  1.24683    3.05545   0.408    0.683    
cluster2    -2.59960    0.62924  -4.131 3.61e-05 ***
cluster3    -0.71255    0.65552  -1.087    0.277    
BMI          0.03651    0.05259   0.694    0.488    
Age         -0.04953    0.14773  -0.335    0.737    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 137.19  on 99  degrees of freedom
Residual deviance: 111.28  on 95  degrees of freedom
  (11 observations deleted due to missingness)
AIC: 121.28

Number of Fisher Scoring iterations: 4

exp(logit_model$coefficients)

(Intercept)    cluster2    cluster3         BMI         Age 
 3.47929507  0.07430301  0.49039354  1.03718010  0.95167251 

exp(confint(logit_model))

                  2.5 %       97.5 %
(Intercept) 0.007890584 1405.7584524
cluster2    0.019615802    0.2383974
cluster3    0.127073175    1.7228302
BMI         0.935752657    1.1525993
Age         0.710311734    1.2750525

Wald test:
----------

Chi-squared test:
X2 = 17.3, df = 2, P(> X2) = 0.00018

# Differential abundance using DESEQ2
require(DESeq2)
M.f.des <- taxa_level(M.std, "G_species")
dds <- phyloseq_to_deseq2(M.f.des, ~Inflammation)
# geometric mean, set to zero when all coordinates are zero
geo_mean_protected <- function(x) {
    if (all(x == 0)) {
        return(0)
    }
    exp(mean(log(x[x != 0])))
}
geoMeans <- apply(counts(dds), 1, geo_mean_protected)
dds <- estimateSizeFactors(dds, geoMeans = geoMeans)
dds <- DESeq(dds, fitType = "local")

res = results(dds, cooksCutoff = FALSE)
df <- as.data.frame(colData(dds)[, "BV"])
colnames(df) <- "BV"

alpha = 0.01
sigtab = as.data.frame(res[which(res$padj < alpha & abs(res$log2FoldChange) > 
    1.5), ])
# sigtab = cbind(as(sigtab, 'data.frame'),
# as(tax_table(M.f.des)[rownames(sigtab), ], 'matrix'))
dim(sigtab)

[1] 15  6

table_sig <- sigtab[c("log2FoldChange", "padj")]
table_sig$Abundant_Group <- levels(df$BV)[as.numeric(sigtab$log2FoldChange > 
    0) + 1]
table_sig %>% kable(booktabs = TRUE, longtable = TRUE) %>% kable_styling(latex_options = c("hold_position", 
    "repeat_header", position = "")) %>% scroll_box(width = "100%", height = "300px")

library("pheatmap")
select <- order(rowMeans(counts(dds, normalized = TRUE)), decreasing = TRUE)[1:50]
df <- as.data.frame(colData(dds)[, c("Inflammation", "BV", "cluster")])

df1 <- data.frame(assay(dds))[select, ]
p <- pheatmap(log2(df1 + 1), cluster_rows = FALSE, show_rownames = TRUE, show_colnames = F, 
    cluster_cols = TRUE, annotation_col = df, annotation_row = NA)
p

require(randomForest)
data.rf <- data.frame(otu_table(M.f.des))
data.rf$BV <- sample_data(M.std)$BV
BV.rf <- randomForest(BV ~ ., data = data.rf, importance = TRUE, proximity = TRUE)
df_accuracy <- as.data.frame(BV.rf$importance)
df_accuracy$Taxa <- rownames(df_accuracy)
df_accuracy <- df_accuracy[order(df_accuracy$MeanDecreaseAccuracy, decreasing = TRUE), 
    ][1:20, ]
df_accuracy$Taxa <- factor(df_accuracy$Taxa, levels = df_accuracy$Taxa)
mda_plot <- ggplot(data = df_accuracy, aes(x = Taxa, y = MeanDecreaseAccuracy)) + 
    theme_bw()
mda_plot <- mda_plot + geom_bar(stat = "identity", fill = "darkblue", width = 0.5)
mda_plot <- mda_plot + theme(axis.text.x = element_text(angle = 90, hjust = 1, 
    vjust = 0.5))
mda_plot <- mda_plot + xlab("") + ylab("Mean Decrease in Accuracy") + coord_flip() + 
    theme(axis.text = element_text(size = 10, face = "bold"), axis.title = element_text(size = 16, 
        face = "bold"))
mda_plot

data.rf_reg <- data.frame(otu_table(M.f.des))
data.rf$BMI <- sample_data(M.f.des)$BMI
BMI.reg <- randomForest(BMI ~ ., data = data.rf, importance = TRUE, proximity = TRUE, 
    na.action = na.omit)
# BMI.reg$importance View(BMI.reg$importance)

R version 3.4.1 (2017-06-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] C

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] randomForest_4.6-14        DESeq2_1.16.1             
 [3] SummarizedExperiment_1.6.5 DelayedArray_0.2.7        
 [5] matrixStats_0.54.0         Biobase_2.36.2            
 [7] GenomicRanges_1.28.6       GenomeInfoDb_1.12.3       
 [9] IRanges_2.10.5             S4Vectors_0.14.7          
[11] BiocGenerics_0.22.1        aod_1.3.1                 
[13] vegan_2.5-4                lattice_0.20-38           
[15] permute_0.9-5              pheatmap_1.0.12           
[17] microbiomeSeq_0.1          factoextra_1.0.5          
[19] cluster_2.0.7-1            plotly_4.9.0              
[21] ggplot2_3.2.0              phyloseq_1.25.2           
[23] kableExtra_1.1.0           rmdformats_0.3.5          
[25] knitr_1.23                

loaded via a namespace (and not attached):
 [1] backports_1.1.4      uuid_0.1-2           Hmisc_4.2-0         
 [4] plyr_1.8.4           igraph_1.2.4         lazyeval_0.2.2      
 [7] sp_1.3-1             splines_3.4.1        BiocParallel_1.10.1 
[10] crosstalk_1.0.0      rncl_0.8.3           digest_0.6.19       
[13] foreach_1.4.4        htmltools_0.3.6      gdata_2.18.0        
[16] memoise_1.1.0        checkmate_1.9.3      magrittr_1.5        
[19] annotate_1.54.0      Biostrings_2.44.2    readr_1.3.1         
[22] gmodels_2.18.1       prettyunits_1.0.2    colorspace_1.4-1    
[25] blob_1.1.1           rvest_0.3.4          ggrepel_0.8.1       
[28] xfun_0.8             dplyr_0.8.0.1        crayon_1.3.4        
[31] RCurl_1.95-4.12      jsonlite_1.6         genefilter_1.58.1   
[34] phylobase_0.8.6      survival_2.44-1.1    iterators_1.0.10    
[37] ape_5.3              glue_1.3.1           gtable_0.3.0        
[40] zlibbioc_1.22.0      XVector_0.16.0       webshot_0.5.1       
[43] seqinr_3.4-5         questionr_0.7.0      dunn.test_1.3.5     
[46] adegraphics_1.0-15   scales_1.0.0         DBI_1.0.0           
[49] miniUI_0.1.1.1       Rcpp_1.0.1           htmlTable_1.13.1    
[52] viridisLite_0.3.0    xtable_1.8-4         progress_1.2.2      
[55] spData_0.3.0         bit_1.1-14           foreign_0.8-71      
[58] spdep_0.7-9          Formula_1.2-3        htmlwidgets_1.3     
[61] httr_1.4.0           RColorBrewer_1.1-2   acepack_1.4.1       
[64] pkgconfig_2.0.2      XML_3.98-1.20        nnet_7.3-12         
[67] deldir_0.1-16        locfit_1.5-9.1       AnnotationDbi_1.38.2
[70] tidyselect_0.2.5     labeling_0.3         rlang_0.3.1         
[73] reshape2_1.4.3       later_0.8.0          munsell_0.5.0       
 [ reached getOption("max.print") -- omitted 56 entries ]